In the field of medical imaging, various imaging modalities and systems are used for generating medical images of anatomical structures of individuals for screening and evaluating medical conditions. These imaging systems include, for example, CT (computed tomography) imaging, MRI (magnetic resonance imaging), NM (nuclear magnetic) resonance imaging, X-ray systems, US (ultrasound) systems, PET (positron emission tomography) systems, etc. For each of these modalities, a specific part of the human body is targeted for imaging, which can be performed in various ways. With ultrasound, sound waves from a transducer are directed towards a specific part of the body (the heart, for example). In MRI, gradient coils are used to “select” a part of the body where nuclear resonance is recorded. The part of the body targeted by the imaging modality usually corresponds to the area that the physician is interested in exploring. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, cardiomyopathy, colonic polyps, aneurisms, lung nodules, calcification on heart or artery tissue, cancer micro calcifications or masses in breast tissue, and various other lesions or abnormalities.
Typically, physicians, clinicians, radiologists, etc, will manually review and evaluate medical images (X-ray films, prints, photographs, etc) reconstructed from an acquired image dataset, to discern characteristic features of interest and detect, diagnose or otherwise identify potential medical conditions. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions, for example. Depending on the skill and knowledge of the reviewing physician, clinician, radiologist, etc., however, manual evaluation of medical images can result in misdiagnosed medical conditions due to simple human error. Furthermore, when the acquired medical images are of low diagnostic quality, it can be difficult for even a highly skilled reviewer to effectively evaluate such medical images and identify potential medical conditions.